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play_gqi.py
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play_gqi.py
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import nibabel as nib
from dipy.reconst.gqi import GeneralizedQSamplingModel
from dipy.reconst.dsi import DiffusionSpectrumDeconvModel
from dipy.data import get_sphere
from dipy.viz.mayavi.spheres import show_odfs
from load_data import get_train_dsi
from dipy.sims.voxel import MultiTensor
data, affine, gtab = get_train_dsi(30)
# data = data[25 - 10:25 + 10, 25 - 10:25 + 10, 25]
# data = data[:, :, 25]
mevals = np.array([[0.0017, 0.0003, 0.0003],
[0.0017, 0.0003, 0.0003],
[0.0017, 0.0003, 0.0003]])
# ang = [(20, 10), (70, 20), (45, 60)]
ang = [(0, 0), (45, 45), (90, 90)]
data, sticks = MultiTensor(gtab, mevals, S0=100, angles=ang,
fractions=[33.3, 33.3, 33.4], snr=100)
gqi_model = GeneralizedQSamplingModel(gtab,
method='gqi2',
sampling_length=4.,
normalize_peaks=False)
gqi_fit = gqi_model.fit(data)
sphere = get_sphere('symmetric724')
gqi_odf = gqi_fit.odf(sphere)
def optimal_transform(model, data, sphere):
from dipy.reconst.gqi import squared_radial_component
H = squared_radial_component
b_vector = model.b_vector
bnorm = np.sqrt(np.sum(b_vector ** 2, axis=1))
# # linearize
# b_vector2 = b_vector * bnorm[:, None]
# b_vector2 /= b_vector.max()
# # push higher q-space values
# bnorm2 = np.sqrt(np.sum(b_vector2 ** 2, axis=1))
# bnorm2n = np.interp(bnorm2, [bnorm2.min(), bnorm2.max()], [0, 1])
# bpush = bnorm2n ** 4
# b_vector3 = b_vector * bpush[:, None]
proj = np.dot(b_vector, sphere.vertices.T)
#proj[np.abs(proj) < 0.1] = 0
#proj = proj * (bnorm[:, None] / 5.)
gqi_vector = np.real(H(proj * model.Lambda / np.pi))
return np.dot(data, gqi_vector), proj
odf, proj = optimal_transform(gqi_model, data, sphere)
from dipy.viz import fvtk
r = fvtk.ren()
fvtk.add(r, fvtk.sphere_funcs(gqi_odf, sphere))
fvtk.show(r)
dsi_model = DiffusionSpectrumDeconvModel(gtab)
dsi_odf = dsi_model.fit(data).odf(sphere)
fvtk.clear(r)
fvtk.add(r, fvtk.sphere_funcs(dsi_odf, sphere))
fvtk.show(r)
fvtk.clear(r)
fvtk.add(r, fvtk.sphere_funcs(odf, sphere))
fvtk.show(r)
def investigate_internals():
def bvl_min_max(b_vector, sphere, sampling_length):
bv = np.dot(gqi_model.b_vector, sphere.vertices.T)
bv.max()
bv.min()
bvl = bv * sampling_length / np.pi
return bvl
def H(x):
return (2 * x * np.cos(x) + (x ** 2 - 2) * np.sin(x)) / x ** 3
sampling_length = 1.2
bvl = bvl_min_max(gqi_model.b_vector, sphere, sampling_length)
x = np.linspace(bvl.min(), bvl.max(), 100)
figure(1)
plot(x, np.sinc(x), 'b', x, H(x), 'g')
title(str(sampling_length))
sampling_length = 3
bvl = bvl_min_max(gqi_model.b_vector, sphere, sampling_length)
x = np.linspace(bvl.min(), bvl.max(), 100)
figure(2)
plot(x, np.sinc(x), 'b', x, H(x), 'g')
title(str(sampling_length))